System and Method for a Self-Optimizing Reservation in Time of Compute Resources

ABSTRACT

A system and method of dynamically controlling a reservation of resources within a cluster environment to maximize a response time are disclosed. The method embodiment of the invention comprises receiving from a requestor a request for a reservation of resources in the cluster environment, reserving a first group of resources, evaluating resources within the cluster environment to determine if the response time can be improved and if the response time can be improved, then canceling the reservation for the first group of resources and reserving a second group of resources to process the request at the improved response time.

PRIORITY CLAIM

The present application is a continuation of U.S. patent application Ser. No. 10/530,581, filed Aug. 11, 2006, which claims priority to U.S. Provisional Application No. 60/552,653 filed. Mar. 13, 2004, the contents of which are incorporated herein by reference in their entirety.

RELATED APPLICATIONS

The present application is related to U.S. patent application Ser. No. 10/530,583; U.S. patent application Ser. No. 10/530; 582; U.S. patent application Ser. No. 10/530,577 (Attorney Docket No. 010-0011C, PCT App. No. PCT/US 05/08288); U.S. patent application Ser. No. 10/530,576; U.S. patent application Ser. No. 10/589,339; U.S. patent application Ser. No. 10/530,578; U.S. patent application Ser. No. 10/530,580, and U.S. patent application Ser. No. 10/530,575, filed on the same day as the present application. The content of each of these cases is incorporated herein by reference.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to reservations in a cluster or more specifically to a system and method of providing a self-optimizing reservation in time of compute resources.

2. Introduction

The present invention relates to a system and method of allocation resources in the context of a grid or cluster of computers. Grid computing may be defined as coordinated resource sharing and problem solving in dynamic, multi-institutional collaborations. Many computing projects require much more computational power and resources than a single computer may provide. Networked computers with peripheral resources such as printers, scanners, I/O devices, storage disks, scientific devices and instruments, etc. may need to be coordinated and utilized to complete a task.

Grid/cluster resource management generally describes the process of identifying requirements, matching resources to applications, allocating those resources, and scheduling and monitoring grid resources over time in order to run grid applications as efficiently as possible. Each project will utilize a different set of resources and thus is typically unique. In addition to the challenge of allocating resources for a particular job, grid administrators also have difficulty obtaining a clear understanding of the resources available, the current status of the grid and available resources, and real-time competing needs of various users. One aspect of this process is the ability to reserve resources for a job. A cluster manager will seek to reserve a set of resources to enable the cluster to process a job at a promised quality of service.

General background information on clusters and grids may be found in several publications. See, e.g., Grid Resource Management, State oldie Art and Future Trends, Jarek Nabrzyski, Jennifer M. Schopf, and Jan Weglarz, Kluwer Academic Publishers, 2004; and Beowulf Cluster Computing with Linux, edited by William Gropp, Ewing Lusk, and Thomas Sterling, Massachusetts institute of Technology, 2003.

It is generally understood herein that the terms grid and cluster are interchangeable in that there is no specific definition of either. In general, a grid will comprise a plurality of clusters as will be shown in FIG. 1A. Several general challenges exist when attempting to maximize resources in a grid. First, there are typically multiple layers of grid and cluster schedulers. A grid 100 generally comprises a group of clusters or a group of networked computers. The definition of a grid is very flexible and may mean a number of different configurations of computers. The introduction here is meant to be general given the variety of configurations that are possible. A grid scheduler 102 communicates with a plurality of cluster schedulers 104A, 104B and 104C. Each of these cluster schedulers communicates with a respective resource manager 106A, 106B or 106C. Each resource manager communicates with a respective series of compute resources shown as nodes 108A, 108B, 108C in cluster 110, nodes 108B, 108E, 108F in cluster 112 and nodes 108G, 108H, 108I in cluster 114.

Local schedulers (which may refer to either the cluster schedulers 104 or the resource managers 106) are closer to the specific resources 108 and may not allow grid schedulers 102 direct access to the resources. Examples of compute resources include data storage devices such as hard drives and computer processors. The grid level scheduler 102 typically does not own or control the actual resources. Therefore, jobs are submitted from the high level grid-scheduler 102 to a local set of resources with no more permissions that then user would have. This reduces efficiencies and can render the reservation process more difficult.

The heterogeneous nature of the shared resources also causes a reduction in efficiency. Without dedicated access to a resource, the grid level scheduler 102 is challenged with the high degree of variance and unpredictability in the capacity of the resources available for use. Most resources are shared among users and projects and each project varies from the other. The performance goals for projects differ. Grid resources are used to improve performance of an application but the resource owners and users have different performance goals: from optimizing the performance for a single application to getting the best system throughput or minimizing response time. Local policies may also play a role in performance.

Within a given cluster, there is only a concept of resource management in space. An administrator can partition a cluster and identify a set of resources to be dedicated to a particular purpose and another set of resources can be dedicated to another purpose. In this regard, the resources are reserved in advance to process the job. There is currently no ability to identify a set of resources over a time frame for a purpose. By being constrained in space, the nodes 108A, 108B, 108C, if they need maintenance or for administrators to perform work or provisioning on the nodes, have to be taken out of the system, fragmented permanently or partitioned permanently for special purposes or policies. If the administrator wants to dedicate them to particular users, organizations or groups, the prior art method of resource management in space causes too much management overhead requiring a constant adjustment the configuration of the cluster environment and also losses in efficiency with the fragmentation associated with meeting particular policies.

To manage the jobs submissions, a cluster scheduler will employ reservations to insure that jobs will have the resources necessary for processing. FIG. 113 illustrates a cluster/node diagram for a cluster 124 with nodes 120. Time is along the X axis. An access control list 114 (ACL) to the cluster is static, meaning that the ACL is based on the credentials of the person, group, account, class or quality of service making the request or job submission to the cluster. The ACL 114 determines what jobs get assigned to the cluster 110 via a reservation 112 shown as spanning into two nodes of the cluster. Either the job can be allocated to the cluster or it can't and the decision is determined based on who submits the job at submission time. The deficiency with this approach is that there are situations in which organizations would like to make resources available but only in such a way as to balance or meet certain performance goals. Particularly, groups may want to establish a constant expansion factor and make that available to all users or they may want to make a certain subset of users that are key people in an organization and want to give them special services but only when their response time drops below a certain threshold. Given the prior art model, companies are unable to have the flexibility over their cluster resources.

To improve the management of cluster resources, what is needed in the art is a method for a scheduler, a cluster scheduler or cluster workload management system to manage resources in a dimensional addition to space. Furthermore, given the complexity of the cluster environment, what is needed is more power and flexibility in the reservations process.

SUMMARY OF THE INVENTION

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The features and advantages of the invention may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the present invention will become more fully apparent from the following description and appended claims, or may be learned by the practice of the invention as set forth herein.

The invention includes systems, methods and computer-readable media embodiments. The method aspect of the invention comprises a method of dynamically controlling a reservation of resources within a compute environment to maximize a response time. The compute environment may be a cluster, grid or any environment of a plurality of compute devices. The method comprises receiving from a requestor a request for a reservation of resources in the cluster environment, reserving a first group of resources and evaluating resources within the cluster environment to determine if the response time can be improved. If the response time can be improved, the method comprises canceling the reservation for the first group of resources and reserving a second group of resources to process the request at the improved response time.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and other advantages and features of the invention can be obtained, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1A illustrates generally a grid scheduler, cluster scheduler, and resource managers interacting with compute nodes;

FIG. 1B illustrates an access control list (ACL) interacting with a group of nodes;

FIG. 2A illustrates a method embodiment of the invention;

FIG. 2B illustrates another method embodiment of the invention;

FIG. 2C illustrates yet another method embodiment of the invention;

FIG. 2D illustrates yet another method embodiment of the invention;

FIG. 2E illustrates yet another method embodiment of the invention;

FIG. 3A illustrates a reservation sandbox;

FIG. 3B illustrates aspects of a reservation sandbox;

FIG. 4 illustrates a rollback reservation;

FIG. 5 illustrates another aspect of the rollback reservation;

FIG. 6 illustrates a dynamic access control list;

FIG. 7 illustrates an interface for creating a reservation;

FIG. 8 illustrates generally a prior art arrangement of clusters in a grid;

FIG. 9 illustrates a trigger attached to an object;

FIG. 10 illustrates an example of the user of triggers according to an aspect of the invention;

FIG. 11 illustrates a method according to an embodiment of the invention; and

FIG. 12 illustrates a graphical user interface used to create triggers.

DETAILED DESCRIPTION OF THE INVENTION

Various embodiments of the invention are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the invention.

The present invention relates to resource reservations in the context of a cluster environment. The cluster may be operated by a hosting facility, hosting center, a virtual hosting center, data center, grid, cluster and/or utility-based computing environments. Software modules and components operate within a computing environment to manage the reservations of resources. The “system” embodiment of the invention may comprise a computing device that includes the necessary hardware and software components to enable a workload manager or a software module performing the steps of the invention. Such a computing device may include such known hardware elements as one or more central processors, random access memory (RAM), read-only memory (ROM), storage devices such as hard disks, communication means such as a modem or a card to enable networking with other computing devices, a bus that provides data transmission between various hardware components, a keyboard, a display, an operating system and so forth. There is no restriction that the particular system embodiment of the invention have any specific hardware components and any known or future developed hardware configurations are contemplated as within the scope of the invention when the computing device operates as is claimed.

An advance reservation is the mechanism by which the present invention guarantees the availability of a set of resources at a particular time. With an advanced reservation a site has an ability to actually specify how the scheduler should manage resources in both space and time. Every reservation consists of three major components, a list of resources, a timeframe (a start and an end time during which it is active), and an access control list (ACL). These elements are subject to a set of rules. The ACL acts as a doorway determining who or what can actually utilize the resources of the cluster. It is the job of the cluster scheduler to make certain that the ACL is not violated during the reservation's lifetime (i.e., its timeframe) on the resources listed. The ACL governs access by the various users to the resources. The ACL does this by determining which of the jobs, various groups, accounts, jobs with special service levels, jobs with requests for specific resource types or attributes and many different aspects of requests can actually come in and utilize the resources. With the ability to say that these resources are reserved, the scheduler can then enforce true guarantees and can enforce policies and enable dynamic administrative tasks to occur. The system greatly increases in efficiency because there is no need to partition the resources as was previously necessary and the administrative overhead is reduced it terms of staff time because things can be automated and scheduled ahead of time and reserved.

As an example of a reservation, a reservation may specify that node002 is reserved for user John Doe on Friday. The scheduler will thus be constrained to make certain that only John Doe's jobs can use node002 at any time on Friday. Advance reservation technology enables many features including backfill, deadline based scheduling, QOS support, and meta scheduling.

There are several reservation concepts that will be introduced as aspects of the invention. These include dynamic reservations, co-allocating reservation resources of different types, reservations that self-optimize in time, reservations that self-optimization in space, reservations rollbacks and reservation masks. Each of these will be introduced and explained.

Dynamic reservations are reservations that are able to be modified once they are created. FIG. 2A illustrates a dynamic reservation. Attributes of a reservation may change based on a feedback mechanism that adds intelligence as to ideal characteristics of the reservation and how it should be applied as the context of its environment or an entities needs change. One example of a dynamic reservation is a reservation that provides for a guarantee of resources for a project unless that project is not using the resources it has been given. A job associated with a reservation begins in a cluster environment (202). At a given portion of time into processing the job on compute resources, the system receives compute resource usage feedback relative to the job (204). For example, a dynamic reservation policy may apply which says that if the project does not use more than 25% of what it is guaranteed by the time that 50% of its time has expired, then, based on the feedback, the system dynamically modifies the reservation of resources to more closely match the job (206). In other words, the reservation dynamically adjust itself to reserve X % fewer resources for this project, thus freeing up unused resource for others to use.

Another dynamic reservation may perform the following step: if usage of resources provided by a reservation is above 90% with fewer than 10 minutes left in the reservation then the reservation will attempt to add 10% more time to the end of the reservation to help ensure the project is able to complete. In summary, it is the ability for a reservation to receive manual or automatic feedback to an existing reservation in order to have it more accurately match any given needs, whether those be of the submitting entity, the community of users, administrators, etc. The dynamic reservation improves the state of the art by allowing the ACL to the reservation to have a dynamic aspect instead of simply being based on who the requestor is. The reservation can be based on a current level of service or response time being delivered to the requestor.

Another example of a dynamic reservation is consider a user submitting a job and the reservation may need an ACL that requires that the only job that can access these resources are those that have a queue time that is currently exceeded two hours. If the job has sat in the queue for two hours it will then access the additional resources to prevent the queue time for the user from increasing significantly beyond this time frame. You can also key the dynamic reservation off of utilization, off of an expansion factor and other performance metrics of the job.

The ACL and scheduler are able to monitor all aspects of the request by looking at, the current job inside the queue and how long it has sat there and what the response time target is. It is preferable, although not required, that the scheduler itself determines whether all requirements of the ACL are satisfied. If the requirements are satisfied, the scheduler releases the resources that are available to the job.

The benefits of this model is it makes it significantly easier for a site to balance or provide guaranteed levels of service or constant levels of service for key players or the general populace. By setting aside certain resources and only making them available to the jobs which threaten to violate their quality of service targets it increases the probability of satisfying it.

Another reservation type is a self optimizing reservation in time. This is shown in FIG. 2B. In many cases, people will request resources and request that they be available at a particular time. For example, a person is doing a demonstration and it happens to be from 2:00 pm to 4:00 pm. In many other cases, people will simply have a deadline or simply want processing as early as possible. With a self-optimizing in time reservation, the scheduler is actually able to lock in a set of resources for a particular request and then over time evaluate the cluster resources and determine if it can actually improve on it and improve on the reservation in such a way as to guarantee that it does not lose the resources that it has already made available.

The method aspect of the invention relates to a method of dynamically controlling a reservation of resources within a cluster environment to maximize a response time for processing the reservation. The method comprises receiving from a requestor a request for a reservation of resources in the cluster environment (210), reserving a first group of resources and guaranteeing to the requestor a response time to process the request (212), evaluating resources within the cluster environment to determine if the response time can be improved (214) and determining whether the response time can be improved (216). If the response time can be improved, the system cancels the reservation for the first group of resources and reserves a second group of resources to process the request at the improved response time (218). If the response time cannot be improved, then the system continues to evaluate the resources according to step 214.

The reservation for the first group of resources and the reservation for the second group of resources can overlap in time and/or in terms of the physical resources, or other resources such as software, or license rights, etc. that are reserved. With self-optimizing reservations in time, a particular request may come in request resources that meet the following criteria but the requester prefers resources that meet a more increasingly strict criteria. The scheduler, in finding the reservation, may be able to satisfy the required criteria but not necessarily satisfy all the preferred criteria. Over time, the scheduler, once it has established a reservation that meets the minimum criteria, it can continue to look at newly freed up resources and determine if it can, to a larger and larger extent, satisfy the preferred resource needs as well.

The self optimizing reservation technology is also useful to work around resource failures in the case of a reservation that has already had reserved all the resources it needs and it has a node failure. Other types of resources failures may also be monitored and reservations modified to meet the original promised quality or service or response time to the requestor. For example, one the requestor submits a request for a reservation of resources, and the system promises a certain response time and reserves a group of resources, the system will monitor those resources for failure. If a component of the group of resources fails, such as a node, or a router, or memory or a CPU, the system will seek to modify the reservation by identifying replacement resources that can be included in the reservation such that the promised quality of service can be met. The system can actually continue to locate resources and reallocate resources that are still up and running and be able to satisfy the time frame it originally promised by excluding the failed node and picking up a newly available compute node. This may be performed by modifying the original reservation, or canceling the original reservation and reserving a second group of resources that excludes the failed resource and includes the reallocated working resource such that the requestor maintains his or her quality of service.

The determination of whether the response time can be improved may includes a comparison of a cost of canceling the first group of resources and reserving the second group of resources with the improved response time gained from meeting at least one of the preferred criteria. In this regard, a threshold value may be established that indicates when overall resource efficiency may be improved in that enough of the preferred criteria may be met to overcome the cost of canceling a reservation and establishing a the new reservation of resources.

FIG. 4 illustrates the roll-back reservation mask 402. An important concept in this type of reservation is that it stays ahead of the current time by either a fixed or dynamic amount of time 408. A method embodiment of this reservation is shown in one aspect in FIG. 2B. As the self-optimization registration slides forward at a fixed (or dynamic) distance in the future (230), it analyses reservations, jobs, objects or other resources 404 (232) and determines whether the level of service may be improved (234). If yes, the method comprises creating a new reservation and making the associated changes (236) and then leaves them 406 behind to be processed when the current time catches up.

A self optimizing reservation will only slide forward barring resource failure of the actual compute resources. It does this by, when it makes a query to determine what resources are available, as part of its algorithm, it determines that it has availability to both free resources and the resources it already has reserved. In such a case in then goes and analyzes it, looks at resources that were recently freed by other workload and other reservations that completed early which is actually quite common in a cluster environment, and if it can find that it can improve the level of service delivered to the request or it will actually create the new reservation and will remove the old reservation and adjust things as needed. A self optimizing reservation therefore has the ability to improve any given attribute of service to the submitting entity, community of users, administrators, etc.

Another aspect of the self-optimizing reservation in time is illustrated in FIG. 2D. A reservation is created for a job on a cluster environment (210). Self optimizing may consist of improving response time, starting the reservation earlier if possible, adding additional resources as available, using fewer resources when fewer resources are required. Another example of a self-optimizing reservation is if an organization were trying to guarantee a specific number of compute nodes, say 32 for instance, and at first look the first time that 32 nodes can be available is in three days, but if they were available earlier the using organization would want them earlier. Other reservations and cluster resources are monitored (212). As time goes on, if another reservation ends its usage earlier and now the 32 node reservation can be set up in only 2 days. The system determines whether an optimization of the reservation exits (214). If yes, that an optimization exists, the reservation self-optimizes (216) and changes its start date ahead by one day. If no optimization can occur, the system continues to monitor the cluster resources (212). Yet another example of a self-optimizing reservation is that a group reserves 8 nodes for 4 hours, but would really prefer to get more nodes to get the work done faster. Due to another reservation concluding early, this reservation can now self-optimize and run the jobs on 32 nodes for just one hour and the using group is done four times faster.

Another reservation is the self-terminating reservation. FIG. 2C illustrates this reservation. A self-terminating reservation is a reservation that can cancel itself if certain criteria take place. As a reservation time begins for a job (220), the system monitors for jobs and cluster resources (222). An example of a self-terminating reservation is a reservation that uses an event policy to check that if after 30 minutes no jobs have been submitted against the reservation, or if utilization of the assigned resources is below x % then the reservation will cancel itself, thus making those resources available to be used by others. Thus, if monitored events justify terminating the reservation (224), then the reservation terminates itself (226). If the monitored events do not justify canceling the reservation, the system continues to monitor events (222).

FIG. 3A illustrates a standing reservation. In cluster 302, there are standing reservations shown as 304A, 304B and 304C. These reservations show resources allocated and reserved on a periodic basis. These are consuming reservations meaning that cluster resources will be consumed by the reservation.

Another embodiment of reservation is something called a reservation mask, which allows a site to create “sandboxes” in which other guarantees can be made. The most common aspects of this reservation are for grid environments and personal reservation environments. In a grid environment, a remote entity will be requesting resources and will want to use these resources on an autonomous cluster for the autonomous cluster to participate. In many cases it will want to constrain when and where the entities can reserve or utilize resources. One way of doing that is via the reservation mask.

FIG. 3B illustrates the reservation mask shown as creating sandboxes 306A, 306B, 306C in cluster 310 and allows the autonomous cluster to state that only a specific subset of resources can be used by these remote requesters during a specific subset of times. When a requester asks for resources, the scheduler will only report and return resources available within this reservation, after which point the remote entity desires it, he can actually make a consumption reservation and that reservation is guaranteed to be within the reservation mask space. The consumption reservations 312A, 312B, 312C, 312D are shown within the reservation masks.

In cluster 310 the reservation masks operate differently from consuming reservations in that they are enabled to allow personal reservations to be created within the space that is reserved. ACL's are independent inside of a sandbox reservation or a reservation mask in that one can also exclude other requesters out of those spaces so they dedicated for these particular users.

The benefits of this approach include preventing local job starvation, and providing a high level of control to the cluster manager in that he or she can determine exactly when, where, how much and who can use these resources even though he doesn't necessarily know who the requesters are or the combination or quantity of resources they will request. The administrator can determine when, how and where requesters will participate in these grids. A valuable use is in the space of personal reservations which typically involves a local user given the authority to reserve a block of resources for a rigid time frame. Again, with a personal reservation mask, the requests are limited to only allow resource reservation within the mask time frame and mask resource set, providing again the administrator the ability to constrain exactly when and exactly where and exactly how much of resources individual users can reserve for a rigid time frame. The individual user is not known ahead of time but it is known to the system, it is a standard local cluster user.

The reservation masks 306A, 306B and 306C define periodic, personal reservation masks where other reservations in a cluster 310 may be created, i.e., outside the defined boxes. These are provisioning or policy-based reservations in contrast to consuming reservations. In this regard, the resources in this type of reservation are not specifically allocated but the time and space defined by the reservation mask cannot be reserved for other jobs. Reservation masks enable the system to be able to control the fact that resources are available for specific purposes, during specific time frames. The time frames may be either single time frames or repeating time frames to dedicate the resources to meet project needs, policies, guarantees of service, administrative needs, demonstration needs, etc. This type of reservation insures that reservations are managed and scheduled in time as well as space. Boxes 308A, 308B, 308C and 308D represent non-personal reservation masks. They have the freedom to be placed anywhere in cluster including overlapping some or all of the reservation masks 306A, 306B, 306C. Overlapping is allowed when the personal reservation mask was setup with a global ACL. A global ACL is an ACL that anyone can use. It is wide open in the sense that anyone can take advantage of the resources within that space. To prevent the possibility of an overlap of a reservation mask by a non-personal reservation, the administrator can set an ACL to constrain it is so that only personal consumption reservations are inside. These personal consumption reservations are shown as boxes 312B, 312A, 312C, 312D which are constrained to be within the personal reservation masks 306A, 306B, 306C. The 308A, 308B, 308C and 308D reservations, if allowed, can go anywhere within the cluster 310 including overlapping the other personal reservation masks. The result is the creation of a “sandbox” where only personal reservations can go without in any way constraining the behavior of the scheduler to schedule other requests.

Another reservation type is the reservation roll-back shown in FIG. 4. This reservation has particular application for enforcing policies or allowing support for service level guarantees in service level agreements. A level of service guarantee allows a site or cluster to guarantee that a particular consumer or organization or type of credential is guaranteed a certain quantity of resources within a certain amount of time. The standard way to provide those guarantees would be to dedicate a block of resources that satisfy the needs and would be statically and rigidly partitioned so that no one else could access it. The request of that organization could not extend beyond the bounds of the dedicated block.

With the present invention regarding the reservation roll-back, an administrator can create a reservation 402 which enforces its policy and continues to float in time a certain distance 408 ahead of the current time. Typically the rectangular area of the reservation has a height that corresponds to guaranteed throughput when processing jobs and the horizontal distance that corresponds to the length in time of the reservation. The reservation 402 may correspond to a certain amount of time according to a service level agreement, such as 3 or 4 months for example. The reservation 402 may extend into infinity as well if there is no defined ending time. The reservation 402 is a provisioning reservation and maintains the time offset 402 to the current time.

To illustrate the reservation roll-back, consider a service level agreement with a company to have twenty resources available within one hour of the request for the resources and that they can make the request anytime. The time offset 408 can then be set to one hour and the company will never will they wait more than one hour to get up to twenty resources. The reservation 402 monitors the resources and when a request is made for resources, consumption reservations 404 are allocated and left behind 406 as the roll-back reservation maintains its offset.

An implementation with reservation rollback would allow a site to set up basically a floating reservation that extends from one hour in the future until a time further in the future, such as 4 or 8 hours in the future, and continues to slide forward in time. The reservation 402 will only allow jobs from this organization can drop down requests or reserve host resources underneath the reservation. As time moves forward, the reservation slides forward in time so it always maintains a constant distance in the future allowing these guarantees 404 to be created and maintained 406 on the cluster.

The time offset 408 may be static or dynamic. A static offset 408 will maintain a constant offset time, such as one hour into the future. The static offset will likely be set by a service level agreement wherein a company requests that the resources become available within an hour. The offset 408 may also by dynamic. There may be requests in the service level agreement where under a given event or set of events, the offset would change wherein the reservation slides closer or farther away from the current time to provide a guarantee of resources within ½ (instead of 1 hour) or 2 hours in the future. There are a variety of ways to vary the offset One can be to simply cancel the current sliding reservation and create a new reservation at a different offset. Another way would be to maintain the current reservation but slide it closer or farther away from the current time. The factors that adjust the dynamic nature of the offset may be based on company requests, the nature and use of the cluster resources, the time the request is made, historical information, and so forth. For example, if the request for resources is made at midnight on a Friday night, perhaps instead of the 1 hour availability of resources, the hosting center analyzes the cluster resources and the time of the request and determines that it can deliver the resources in ½. The company may want a flexible offset where if the request is made during a block of time such as between 3-4:30 pm (near the end of the work day) that the offset be shorted so that the job can be processed sooner. The modifications to the offset may be automatic based on a feedback loop of information or may be adjustable by an administrator.

The reservation rollback policy mask is stackable allowing multiple different types of service or service level agreements to be simultaneously satisfied and share a collection of resources. This feature is illustrated in FIG. 5. A reservation 502 is shown and can generally be considered as an aggregation of requests from various masks 504, 506, 508 510. These are aggregated into one space 502 which will then allow reservations to be created on a first come first serve basis, or based on other factors. If these reservation masks 504, 506, 508 and 510 are stacked with individual offsets from the current time (not shown), the administrator can allow the masks to be partitioned among consumers, A useful component of this stackable approach is the capability to have an enveloping reservation 502 created with a total quantity of resource and rollback time offset 408 and a duration to the end of the SLA. Once that reservation space is established or paid for, as a service, the hosting center sub-partitions the space using reservation to provide service guarantees, response time guarantees, quantity or resources guarantees taking advantage of the stacking capability.

A company may therefore establish the enveloping reservation 502 and request from the hosting center that they partition the space according to various organizations within the enveloping reservation 502. This eliminates the need for a large entity to have its own group of clusters of computer.

As mentioned above, the present application is related to U.S. patent application Ser. No. 10/530,578, which was incorporated herein by reference. The following paragraphs, modified for formatting, are from that application.

The present invention provides for systems and methods of dynamically controlling a cluster or grid environment. The method comprises attaching a trigger to an object and firing the trigger based on a trigger attribute. The cluster environment is modified by actions initiated when the trigger is fired. Each trigger has trigger attributes that govern when it is fired and actions it will take. The use of triggers enables a cluster environment to dynamically be modified with arbitrary actions to accommodate needs of arbitrary objects. Example objects include a compute node, compute resources, a cluster, groups of users, user credentials, jobs, resources managers, peer services and the like.

The present invention relates to triggers in the context of compute resource management and more specifically to a system and method of generating triggers which could be attached to any other scheduling object.

The present invention applies to computer clusters and computer grids. A computer cluster may be defined as a parallel computer that is constructed of commodity components and runs commodity software. FIG. 8 illustrates in a general way an example relationship between clusters and grids. A cluster 810 is made up of a plurality of nodes 808A, 808B, 808C, each containing computer processors, memory that is shared by processors in the node and other peripheral devices such as storage discs connected by a network. A resource manager 806A for the node 810 manages jobs submitted by users to be processed by the cluster. Other resource managers 806B, 806C are also illustrated that may manage other clusters (not shown). An example job would be a weather forecast analysis that is compute intensive that needs to have scheduled a cluster of computers to process the job in time for the evening news report.

A cluster scheduler 804A may receive job submissions and identify using information from the resource managers 806A, 806B, 806C which cluster has available resources. The job would then be submitted to that resource manager for processing. Other cluster schedulers 804B and 804C are shown by way of illustration. A grid scheduler 802 may also receive job submissions and identify based on information from a plurality of cluster schedulers 804A, 804B, 804C which clusters may have available resources and then submit the job accordingly.

Grid/cluster resource management generally describes the process of identifying requirements, matching resources to applications, allocating those resources, and scheduling and monitoring grid resources over time in order to run grid applications as efficiently as possible. Each project will utilize a different set of resources and thus is typically unique. In addition to the challenge of allocating resources for a particular job, grid administrators also have difficulty obtaining a clear understanding of the resources available; the current status of the grid and available resources, and real-time competing needs of various users.

Several books provide background information on how to organize and create a cluster or a grid and related technologies. See, e.g., Grid Resource Management, State of the Art and Future Trends, Jarek Nabrzyski, Jennifer M. Schopf, and Jan Weglarz, Kluwer Academic Publishers, 2004; and Beowulf Cluster Computing with Linux, edited by William Gropp, Ewing Lusk, and Thomas Sterling, Mass, institute of Technology, 2003.

Virtually all clusters have been static which means that an administrator establishes the policies for the cluster, sets up the configuration, determines which nodes have which applications, how much memory should be associated with each node, which operating system will run on a node, etc. The cluster will stay in the state determined by the administrator for a period of months until the administrator takes the entire machine off-line to make changes or modifications. Then the machine is brought back on-line where another 10,000-100,000 jobs may be run on it.

Within this static cluster environment, there is the ability to have something called a job step, a job step allows an application to prepare or modify its environment within the constraints of the compute resources provided by the cluster. For example a job may consist of three steps, the first step is puffing data off of a storage system and transferring the data onto a local file system. The second step may actually process the data and a third step may take the data and go through a second processing step and push it back out to a storage system. These job steps enable some additional functionality for the job in that it allows a job to work within the environment they have.

However, there are some deficiencies in this process. Using job steps does nothing for allowing the jobs to actually change the compute environment provided by the cluster in any way. Job steps operate within the cluster environment but have no control or ability to maximize efficiencies within the environment or adjust the environment. They are static in the sense that they are limited to manipulation of tasks within the given cluster environment. What is needed in the art is a method of improving the efficiency of the compute environment via a device associated with a job or other object.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The features and advantages of the invention may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the present invention will become more fully apparent from the following description and appended claims, or may be learned by the practice of the invention as set forth herein.

The present invention addresses the deficiencies in the art discussed above. The cluster that receives a job submission according to the present invention is dynamic in that the cluster and the resources associated with the cluster may dynamically modify themselves to meet the needs of the current workload. To accomplish this dynamic component of the cluster, the present invention further involves introducing triggers.

A trigger is an object which can be attached or associated with any other scheduling object. A scheduling object can be, for example, one of: a compute node, compute resources, a reservation, a duster, user credentials, groups or accounts, a job, a resource manager, other peer services and the like. Any scheduling object can have any number of triggers associated with it.

The invention comprises various embodiments associated with dynamic clusters and triggers. These embodiments include systems, methods and computer-readable media that provide the features of the invention. The method embodiment of the invention comprises a method for dynamically modifying a cluster, the method comprising attaching a trigger to a scheduling object and firing the trigger based on a trigger attribute, wherein the cluster environment is modified by an action taken by the trigger.

Various embodiments of the invention are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the invention.

The “system” embodiment of the invention may comprise a computing device that includes the necessary hardware and software components to enable a workload manager or a software module performing the steps of the invention. A workload manager manages the compute environment by reserving, at a first time, resources in the compute environment to yield a reservation. The resources associated with the reservation are then consumed by jobs at a second time which is later than the first time. Such a computing device may include such known hardware elements as one or more central processors, random access memory (RAM), read-only memory (ROM), storage devices such as hard disks, communication means such as a modem or a card to enable networking with other computing devices, a bus that provides data transmission between various hardware components, a keyboard, a display, an operating system and so forth. There is no restriction that the particular system embodiment of the invention has any specific hardware components and any known or future developed hardware configurations are contemplated as within the scope of the invention when the computing device operates as is claimed.

The present invention enables the dynamic modification of compute resources within a compute environment such as a cluster or a grid by the use of triggers. FIG. 9 illustrates a trigger 904 being attached to an object 902. The object 902 is preferable a scheduling object and each trigger 904 is configured with a plurality of attributes. Example objects include a compute node, a reservation within a cluster, a cluster itself, a user, a job submitted by a user to a cluster manager, a resource manager, etc. As can be appreciated, an “object” in the context of cluster management may be any number of concepts to which a trigger may be attached.

An example attribute associated with a trigger includes an event, type, which means that one would like this trigger to fire or execute based on a particular event occurring such as the creation of the object, the starting, execution, cancellation or termination of an object, or an object state.

Other attributes associated with a trigger include a time-out, an offset feature, a particular action (such as send an e-mail to the administrator), dependencies, an argument list, a state and a threshold value. This is not meant to be an exhaustive complete list. Other attributes may also be attached to the trigger. For example, meaning dependencies can be based on attributes within the object, wherein if a job is now running, a dependency may be that it fires if a parameter is set to “true”. In that case, the trigger also has a variable it sets to cascade other triggers by setting variables that cause other triggers to fire. Such parameters may relate to things like a threshold, a re-arm time, time-out values and durations. In this manner, a cascade of triggers may fire based on various modified and set parameter from one trigger to the next. Other values that may be used to fire triggers include such parameters as: user credentials, jobs, groups, jobs per user and other types of thresholds. For example, whenever a user exceeds X number of jobs, launch a trigger to take an action. A group-based parameter example is: (1) if user John has more than 18 idle jobs, then send a note to an administrator; and (2) if a group “staff” resource availability query receives a reply with resources more than two hours out, then launch a trigger to modify reservation Y to provide more resources.

The offset feature involves establishing that the trigger will fire either before or after an event has occurred. The example trigger in FIG. 10 illustrates their use in a hosting environment in which a customer wants to reserve a block of resources for a particular time frame and the administrator wants to dynamically provision those resources. FIG. 10 illustrates a reservation 1002 that is processing in time. A trigger 1004 is attached to the object with attributes including an offset to begin a certain period of time (say two minutes) 1012 after the reservation 1002 begins its process. The trigger 304 has as an attribute an action to take which is to set up a network and generate an ARGLIST variable called $IPlist and return that value to the reservation environment. The trigger 1004 also transmits the $IPList to another trigger 1006. The trigger 1006 has a start time offset but also a dependency that it does not fire until the $IPList variable is set. Once the variable is set, the trigger 1006 sets up a storage area network, brings in the resources and makes the resources available to the reservation. When trigger 1006 completes, a third trigger 1008 performs an operating system setup, which also has a dependency on the $IPList variable being set to a value as well as a variable being set to “true”. When both of those parameters are satisfied, trigger 1008 fires and sets up the operating system and application environment and completes. The output of trigger 1008 is a parameter stating whether the operating system setup was successful (“true”) or not.

Independent of these triggers is an additional trigger 1010 that is set to fire at a fixed offset from the start of the reservation, and it performs a health check to verify that the OS setup variable which is setup by the trigger 1008 is true. If it is not set to true, then trigger 1010 is designed to do two things: (1) cancel the reservation itself and send an e-mail to the administrator and end user notifying them that there has been a failure and the reservation will not be available; and (2) retry the initial setup triggers or look for additional local in time at which these blocked resources could be made available and send an e-mail to the user saying we'll retry at this particular time. All of this is performed automatically through the use of triggers.

The above example provides an illustration of the various features of triggers, including the ability to start at an offset value, perform certain actions, having certain dependencies based on data being processed and received or other kinds of dependencies and produce and receive argument lists.

In addition, triggers can specify arbitrary actions allowing it to modify the scheduling state, to execute some process, to pull something in from off the Internet or to update a database. Any arbitrary action that can modify the environment, including destroying the object or reconfiguring the object. Furthermore, triggers have the ability to specify dependencies, saying the trigger can only fire when an event has occurred, the offset has been satisfied and certain other conditions such as variables have been set or other triggers completed with certain states. Each trigger can begin with a variable called in from an ARGLIST which allows you to pass in either static or dynamic variables to modify its behavior.

Also associated with triggers is the concept of a trigger timeout. This feature allows one to determine if a trigger has not fired yet or if it has completed successfully, unsuccessfully or if it's still in process of completing. With all these capabilities, an administrator can have essentially arbitrary control over decision making and process flow to modify the dynamic cluster environment in any way desired.

There are a number of ways to create a trigger, FIG. 12 illustrates a graphical tool 1200 to simply point and click to associate the trigger and attach it to an object. The tool allows the user to select: the creation of a trigger when a reservation starts (or other selectable time via a drop down menu) 1202, the trigger start time for a certain number of minutes before or after a reservation starts 1204, an action launched by a trigger such as to cancel the reservation 1206, an executable file to execute 1208 or to receive an argument list 1210 and a reservation utilization threshold 1212.

Any action may launch a trigger. For example, if a resource manager goes down, or is a software license is about to expire, or a software application that is going to have a job executed with use of the software and it is out-of-date. Any event may launch a trigger.

The second method is to set it up in a configuration file a Moab™ configuration file is simply a flat text file which specifies associations and definitions of triggers. A third way is to simply use command line arguments to generate a trigger. These triggers can be created remotely over the network interface or locally. The following is an example of a command line method of creating triggers by user “Smith”:

mrsvctl -c -h smith -T \ ′Sets=$Var1.War2.$Var3.!Net,EType=start,AType=exec,Action=itmp/Net.sh,Timeout=10′\

-T \

Requires=$Var1.$Var2.$Var3,Sets=$Var4.$Var5,EType=start,AType=exec, Action=/tmp/FS.sh′\

-T \

Requires=$Var1.$Var2.$Var3,Var4.$Var5,Sets=!NOOSinit.OSinit,

Etype=start,AType=exec,Action= /tmp/OS.sh+$Var1:$Var2:$Var3:$Var4:$Var5′ \

-T \

Requires=failed,AType=cancel,EType=start \

-T \

Eype=start,Requires=OSinit,AType=exec,Action=/tmp/success.sh \

-T \

Requires=Net,EType=start,Sets=failed,AType=exec,Action=/tmp/fail.sh

This demonstrates a string of triggers, the first two set variables, the third one requires each of those variables to be set and there are also triggers that activate in case of failure.

An important feature that differentiates triggers from the job step is that there are other systems that allows one to have some sense of dependencies and modification but that is only within a single, given application or job. Job steps can modify their own data and the like but there's nothing that can modify either scheduling policy or scheduling objects, or scheduling environment, like triggers can. Triggers allow one to take any arbitrary action based on any arbitrary set of sensors. Triggers enable puffing in a wide ranging scope of information and having a wide scope of control. They are preferable written in the “c” programming language but there are no constraints on the type of programming language.

One of the attributes introduced above that is associated with a trigger is the threshold attribute. In addition to being able to say that a trigger will fire, when its dependencies are satisfied and its event has occurred and its offset has been satisfied, one may also specify whether a particular threshold and its threshold criteria has been satisfied. This feature allows one to have triggers that fire when particular qualities of service are not satisfied, when queue times have been exceeded, when anything that correlates to basically system performance has or has not been satisfied. When these metrics have not been satisfied or have been satisfied this provides some way one can have arbitrary actions occur.

Other examples of trigger usage are that an administrator can attach a trigger to a node and allow a node monitor such as Ganglia to perform monitoring activities such as detecting keyboard touches. So if a local user has begun to type or if the system detects a high level of data transmission or swapping, a trigger action may adjust the priority of that node so that it is no longer as likely to be selected for batch work load. The priority adjustment may reduce the probability that the node would be selected for a large job like a batch work load.

Performance triggers illustrate another type of trigger that is associated with a particular group or a particular user and a threshold parameter. The parameter may be a performance threshold parameter that is related to, for example, an average response time that is below a particular threshold. If that particular threshold is not satisfied, then the trigger fires and sends an e-mail off to an administrator and adjusts the priority of that user's jobs. The trigger may also dynamically modify the cluster resources to accommodate the at least one user's activities so that the user experiences a performance level at least at the threshold parameter.

As mentioned above, the present application is related to U.S. patent application Ser. No. 10/530,583, which was incorporated herein by reference. The following paragraphs, modified for formatting, are from that application.

In addition to standing and administrative reservations, a workload manager according to the invention can also create priority reservations. These reservations are used to allow the benefits of out-of-order execution (such as is available with a backfill feature) without the side effect of job starvation. Starvation can occur in any system where the potential exists for a job to be overlooked by the scheduler for an indefinite period. In the case of backfill, small jobs may continue to be run on available resources as they become available while a large job sits in the queue never able to find enough nodes available simultaneously to run on. To avoid such situations, priority reservations are created for high priority jobs which cannot run immediately. When making these reservations, the scheduler determines the earliest time the job could start, and then reserves these resources for use by this job at that future time. By default, only the highest priority job will receive a priority reservation. However, this behavior is configurable via a reservation depth policy. The workload manager's default behavior of only reserving the highest priority job allows backfill to be used in a form known as liberal backfill. This liberal backfill tends to maximize system utilization and minimize overall average job turnaround time. However, it does lead to the potential of some lower priority jobs being indirectly delayed and may lead to greater variance in job turnaround time. A reservation depth parameter can be set to a very large value, essentially enabling what is called conservative backfill where every job which cannot run is given a reservation. Most sites prefer the liberal backfill approach associated with the default reservation depth 1 or select a slightly higher value. It is important to note that to prevent starvation in conjunction with reservations, monotonically increasing priority factors such as queuetime or job x-factor should be enabled.

Another important consequence of backfill and reservation depth is its affect on job priority. In the workload manager, all jobs are preferably prioritized. Backfill allows jobs to be run out of order and thus, to some extent, job priority to be ignored. This effect, known as ‘priority dilution’ can cause many site policies implemented via workload manager prioritization policies to be ineffective. Setting the reservation depth parameter to a higher value will give job priority ‘more teeth’ at the cost of slightly lower system utilization. This lower utilization results from the constraints of these additional reservations, decreasing the scheduler's freedom and its ability to find additional optimizing schedules. Anecdotal evidence indicates that these utilization losses are fairly minor, rarely exceeding 8%.

In addition to the reservation depth parameter, sites also have the ability to control how reservations are maintained. The workload manager's dynamic job prioritization allows sites to prioritize jobs so that their priority order can change over time. It is possible that one job can be at the top of the priority queue for a time, and then get bypassed by another job submitted later. A reservation policy parameter allows a site to determine what how existing reservations should be handled when new reservations are made. The value “highest” will cause that all jobs which have ever received a priority, reservation will maintain that reservation until they run even if other jobs later bypass them in priority value. The value of the parameter “current highest” will cause that only the current top <RESERVATIONDEPTH> priority jobs will receive reservations. If a job had a reservation but has been bypassed in priority by another job so that it no longer qualifies as being among the top <RESERVATIONDEPTH> jobs, it will lose its reservation. Finally, the value “never” indicates that no priority reservations will be made.

QOS-based reservation depths can be enabled via the reservation QOS list parameter. This parameter allows varying reservation depths to be associated with different sets of job QoS's. For example, the following configuration will create two reservation depth groupings:

- - - -

RESERVATIONDEPTH[0] 8

RESERVATIONQOSLIST[0] highprio interactive debug

RESERVATIONDEPTH[1] 2

RESERVATIONQOSLIST[1] batch - - - -

This example will cause that the top 8 jobs belonging to the aggregate group of highprio, interactive, and debug QoS jobs will receive priority reservations. Additionally, the top 2 batch QoS jobs will also receive priority reservations. Use of this feature allows sites to maintain high throughput for important jobs by guaranteeing a significant proportion of these jobs are making progress toward starting through use of the priority reservation. The following are example default values for some of these parameters: RESERVATIONDEPTH[DEFAULT]=1; RESERVATIONQOSLIST[DEFAULT]=ALL.

This allows one job with the highest priority to get a reservation. These values can be overwritten by modifying the default policy.

Embodiments within the scope of the present invention may also include computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code means in the form of computer-executable instructions or data structures. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or combination thereof) to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of the computer-readable media.

Computer-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments. Generally, program modules include routines, programs, objects, components, and data structures, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.

Those of skill in the art will appreciate that other embodiments of the invention may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Embodiments may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Although the above description may contain specific details, they should not be construed as limiting the claims in any way. Other configurations of the described embodiments of the invention are part of the scope of this invention. Accordingly, the appended claims and their legal equivalents should only define the invention, rather than any specific examples given. 

1-20. (canceled)
 21. A method of operating a compute environment comprising a plurality of compute nodes under common administrative control, the method comprising: receiving a request for processing one or more workload items; causing processing of at least one of the one or more workload items submitted within the request using at least a portion of the compute environment; monitoring at least one performance attribute after causing the processing; and based at least on the monitored at least one performance attribute, causing dynamic modification of at least a portion of the compute environment.
 22. The method of claim 21, wherein: the receiving a request comprises receiving a request having at least one quality of service (QoS) requirement associated therewith; the monitoring the at least one performance attribute after causing the processing comprises monitoring the processing of the one or more workload items relative to the at least one QoS requirement; and the causing dynamic modification of the at least portion of the compute comprises causing dynamic modification of the compute environment to increase or enhance performance of at least one aspect of the compute environment.
 23. The method of claim 21, further comprising causing utilization of a reservation associated with the processing of the one or more workload items; and wherein the causing dynamic modification of the at least portion of the compute environment comprises causing dynamic modification of a priority associated with at least a portion of the reservation.
 24. The method of claim 21, further comprising: causing utilization of a reservation associated with the processing of the one or more workload items; and associating a trigger with the reservation, the trigger comprising at least one condition for firing; wherein the causing dynamic modification of the at least portion of the compute environment comprises causing dynamic modification of a priority of at least one of the one or more workload items based at least on the monitoring indicating that the at least one condition for firing has been met.
 25. The method of claim 21, further comprising: causing utilization of a software object associated with scheduling of the processing of the one or more workload items; and associating a trigger with the software object, the trigger comprising a second software object specifying at least one condition for firing; wherein the causing dynamic modification of the at least portion of the compute environment comprises causing dynamic modification of a priority of at least one of the one or more workload items based at least on the monitoring indicating that the at least one condition for firing has been met.
 26. The method of claim 21, further comprising causing placing, prior to the causing processing, at least some resources of the compute environment in a reserved state for the processing of the one or more workload items.
 27. The method of claim 21, further comprising causing placing at least one of the one or more workload items in a queue for processing; and wherein: the performance attribute relates to at least one QoS requirement; and the causing dynamic modification of the at least portion of the compute environment comprises causing dynamic modification of a priority of the at least one of the one or more workload items within the queue based at least on the monitoring indicating that the at least one QoS requirement is not being met.
 28. The method of claim 21, wherein: the receiving a request comprises receiving a request having at least one quality of service (QoS) requirement associated therewith; the causing processing of the at least one of the one or more workload items comprises assigning a first resource configuration to the at least portion of the compute environment for processing the at least one of the one or more workload items; the at least one performance attribute relates to processing of the one or more workload items relative to the at least one QoS requirement; and the causing dynamic modification of the at least portion of the compute environment comprises causing dynamic modification of the compute environment in order to assign a second resource configuration to at least part of the compute environment for processing of the one or more of the workload items.
 29. The method of claim 21, wherein: the at least one performance attribute comprises a level of processing activity associated with at least one of the plurality of compute nodes; and the causing dynamic modification of the at least portion of the compute environment comprises causing dynamic modification of the compute environment in order to reduce a likelihood that the at least one of the plurality of compute nodes will be subsequently selected for processing workload.
 30. The method of claim 29, wherein: the level of processing activity associated with at least one of the plurality of compute nodes comprises a level of processing activity associated with the at least one of the plurality of compute nodes during processing of the one or more workload items; and the causing dynamic modification of the compute environment in order to reduce a likelihood that the at least one of the plurality of compute nodes will be subsequently selected for processing workload comprises reducing a priority associated with the at least one compute node relative to others of the plurality of compute nodes.
 31. The method of claim 29, wherein the level of processing activity associated with at least one of the plurality of compute nodes comprises at least one of: (i) a level of data transmission associated with the at least one compute node, or (ii) a level of data swapping associated with the at least one compute node.
 32. The method of claim 21, wherein: the method further comprises associating a trigger with at least one of the plurality of compute nodes, the trigger comprising at least one condition for firing, the at least one condition for firing comprising a level of processing activity associated with at least one compute node; the at least one performance attribute comprises the level of processing activity associated with at least one compute node; and the causing dynamic modification of the at least portion of the compute environment comprises causing reduction of a likelihood that the at least one compute node will be subsequently selected for processing workload, the causing reduction responsive to the monitoring determining that the level of processing activity associated with at least one compute node has been met or exceeded.
 33. A method of operating a compute environment comprising a plurality of compute nodes under common administrative control, the method comprising: receiving a request for processing one or more workload items, the request comprising data indicative of a required level of processing performance; causing processing of at least one of the one or more workload items submitted within the request using at least a portion of the compute environment; monitoring one or more events after causing the processing; and based at least on the monitored one or more events, causing dynamic modification of at least a priority associated with the request.
 34. The method of claim 33, wherein: the compute environment further comprises at least one of a computerized workload manager process or computerized scheduler process; and the priority associated with the request is based at least on a user-specified parameter relating to configuration of the at least one of a computerized workload manager process or computerized scheduler process.
 35. The method of claim 33, wherein the priority associated with the request is based at least on parameter relating to a required level of performance for processing the request.
 36. The method of claim 35, wherein the parameter relating to a required level of performance for processing the request comprises a user-specified parameter which is set using at least a computerized user process in data communication with the compute environment.
 37. The method of claim 33, wherein: the receiving a request comprises receiving a request having at least one quality of service (QoS) requirement associated therewith; the causing processing of the at least one of the one or more workload items comprises assigning a first priority to the at least one of the one or more workload items; the monitoring one or more events after causing the processing comprises monitoring the processing of the one or more workload items relative to the at least one QoS requirement; and the causing dynamic modification of at least a priority associated with the request comprises causing dynamic modification of the first priority.
 38. The method of claim 37, wherein the causing dynamic modification of the first priority comprises increasing the first priority of the at least one of the one or more workload items to a second priority, the second priority greater than the first priority.
 39. The method of claim 37, wherein the causing dynamic modification of the first priority comprises dynamic modification of at least part of a computerized scheduler process associated with the compute environment.
 40. The method of claim 33, further comprising causing utilization of a reservation associated with the processing of the one or more workload items; and wherein the causing dynamic modification of the at least priority associated with the request comprises causing dynamic modification of a priority associated with the reservation.
 41. The method of claim 33, further comprising: causing utilization of a reservation associated with the processing of the one or more workload items; and associating a trigger with the reservation, the trigger comprising at least one condition for firing; wherein the causing dynamic modification of the at least priority associated with the request based at least on the monitored one or more events, comprises causing dynamic modification of a priority of at least one of the one or more workload items based at least on the monitoring indicating that the at least one condition for firing has been met.
 42. The method of claim 21, further comprising: causing utilization of a software object associated with scheduling of the processing of the one or more workload items; and associating a trigger with the software object, the trigger comprising a second software object specifying at least one condition for firing; wherein the causing dynamic modification of at least one of the compute environment or a priority associated with the request based at least on the monitored one or more events, comprises causing dynamic modification of a priority of at least one of the one or more workload items based at least on the monitoring indicating that the at least one condition for firing has been met.
 43. A method of operating a compute environment comprising a plurality of resources including a plurality of compute nodes, the method comprising: creating a trigger, the trigger comprising at least one attribute relating to a level of compute node processing activity; associating the trigger with at least one of the plurality of compute nodes; monitoring the level of compute node processing activity for the at least one compute node; and based at least on the monitored level of compute node processing activity meeting at least one prescribed criterion, causing the execution of the trigger, the execution causing one or more modifications to the compute environment to be initiated; wherein the one or more modifications to the compute environment comprise modification in order to reduce a likelihood that the at least one compute node will be subsequently selected for processing workload.
 44. The method of claim 43, wherein the method further comprises: receiving a request for processing, by the compute environment, of one or more compute jobs; and based at least on the received request, causing processing of at least one of the one or more compute jobs on one or more selected compute nodes of the compute environment, a selection of the one or more selected compute nodes being based at least on the modification.
 45. The method of claim 44, wherein the modification in order to reduce the likelihood that the at least one compute node will be subsequently selected for processing workload comprises a modification of a priority associated with the at least one compute node.
 46. The method of claim 44, wherein the monitoring of a level of processing activity associated with at least one of the plurality of compute nodes comprises monitoring at least one of: (i) a level of data transmission associated with the at least one compute node, or (ii) a level of data swapping associated with the at least one compute node.
 47. The method of claim 43, wherein the creating a trigger comprises creating a software function via a user input to a computerized user process in data communication with the compute environment.
 48. The method of claim 43, wherein the modification in order to reduce a likelihood that the at least one compute node will be subsequently selected for processing workload comprises a dynamic modification to one of a (i) computerized workload manager process of the compute environment, or (ii) computerized scheduler process of the compute environment.
 49. The method of claim 43, wherein the modification in order to reduce a likelihood that the at least one compute node will be subsequently selected for processing workload comprises a modification in order to reduce a likelihood that the at least one compute node will be subsequently selected for processing of a particular type of workload.
 50. The method of claim 49, wherein the particular type of workload comprises a batch workload. 